Long-term maintenance of the analgesic effects of transcranial magnetic stimulation in fibromyalgia
Bibliographic record
Abstract
We assessed for the first time the long-term maintenance of repetitive transcranial magnetic stimulation (rTMS)-induced analgesia in patients with chronic widespread pain due to fibromyalgia. Forty consecutive patients were randomly assigned, in a double-blind fashion, to 2 groups: one receiving active rTMS (n=20) and the other, sham stimulation (n=20), applied to the left primary motor cortex. The stimulation protocol consisted of 14 sessions: an "induction phase" of 5 daily sessions followed by a "maintenance phase" of 3 sessions a week apart, 3 sessions a fortnight apart, and 3 sessions a month apart. The primary outcome was average pain intensity over the last 24 hours, measured before each stimulation from day 1 to week 21 and at week 25 (1 month after the last stimulation). Other outcomes measured included quality of life, mood and anxiety, and several parameters of motor cortical excitability. Thirty patients completed the study (14 in the sham stimulation group and 16 in the active stimulation group). Active rTMS significantly reduced pain intensity from day 5 to week 25. These analgesic effects were associated with a long-term improvement in items related to quality of life (including fatigue, morning tiredness, general activity, walking, and sleep) and were directly correlated with changes in intracortical inhibition. In conclusion, these results suggest that TMS may be a valuable and safe new therapeutic option in patients with fibromyalgia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".